Data Engineer II
New
J
JobgetherData engineering
Listing location: Canada; Workplace type: Remote; Structured job location: CanadaFull-TimeMiddle
Salary not disclosed
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Job Details
- Experience
- 4–5+ years of professional experience in data engineering or software development within a commercial environment.
- Required Skills
- AWSPythonSQLHadoopJavaSnowflakeBigQuery
Requirements
- Bring 4–5+ years of professional data engineering or software development experience in a commercial environment.
- Demonstrate hands-on proficiency in Java and Python for production systems.
- Have experience designing and implementing complex ETL/ELT processes from concept through production.
- Have strong SQL skills and experience exploring large, complex datasets.
- Bring experience with Hadoop, Hive, BigQuery, and Snowflake, including terabyte-scale or larger datasets.
- Understand data structures, algorithms, and object-oriented design.
- Have professional experience developing REST services and working with event queue systems.
- Be familiar with Linux and cloud infrastructure design, preferably AWS or an equivalent platform.
- Have experience with system performance, optimization, and tuning, and understand how architecture affects scalability.
- Hold a Bachelor’s degree in Computer Science, Engineering, or equivalent practical experience.
- Stream processing experience with Flink or Spark Streaming is an asset.
- Experience with Apache Airflow or similar workflow orchestration tools is an asset.
- Data governance, large-scale data processing infrastructure, or high-volume, low-latency systems experience is an asset.
Responsibilities
- Design, build, and maintain ETL/ELT pipelines processing terabyte-scale data across Snowflake, BigQuery, Hive, and other platforms.
- Develop reusable data models and curated datasets for analytics, data science, CRM, machine learning, and other internal consumers.
- Own pipeline lifecycles, including SLAs, performance measurement, monitoring, and anomaly detection.
- Ensure data integrity, validation, documentation, and governance.
- Develop production Java and Python applications for data ingestion, event processing, REST services, and internal tooling.
- Build and maintain streaming and batch processing systems using Flink, Spark, and Kafka.
- Own architecture, implementation, QA, maintenance, and production releases through CI/CD practices.
- Operate and improve AWS cloud infrastructure using Linux, Gradle, and related engineering tools.
- Contribute to technical design discussions, code reviews, and engineering best practices.
- Collaborate with Product, Design, Analytics, Data Science, and engineering teams to define requirements and deliver solutions.
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